We are so passionate about our field of work that we continually update our knowledge of products, hardware and other relevant IT developments. We realise that this can benefit you too, by sharing targeted information about value and necessity, pros and cons. Our professionals enjoy sharing their knowledge here on YouTube with you.
We also regularly organise knowledge sessions, events and trainings, on our own initiative or at your request. Visit our website: www.dm-p.com for more information.

published:04 Jul 2014

views:870

“Introduction to Data Management” is designed for both, business and technology professionals, who are looking for understanding of data management fundamental aspects.

ModernMark continues his Journey to Modern Marketing in Episode 8: Data Management. Watch him use data management and activation to create more meaningful customer interactions using Modern Marketing best practices and data-driven personalization.

published:22 Mar 2016

views:1313

The main challenge of Big Data is storing and processing the data at a specified time span. The traditional approach is not efficient in doing that. So Hadoop technologies and various Big Data tools have emerged to solve the challenges in Big Data environment. There are a lot of Big Data tools, all of them help in some or the other way in saving time, money and in covering business insights. This video will talk about such tools used in Big Data management.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Tools-Tutorial-Pyo4RWtxsQM&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

published:02 Nov 2017

views:1850

Do you have the right data? Check. Do you have the skills onboard to effectively mine the data? Check. Then you might just be ready for predictive analytics.

published:12 May 2017

views:149

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuable foresights that they never could have in the past. While it is easy to leverage Hadoop for crunching large volumes of data, organizing data, managing life cycle of data and processing data is fairly involved. This is solved adequately well in a traditional data platform involving data warehouses and standard ETL (extract-transform-load) tools, but remains largely unsolved today. Besides data processing complexities, Hadoop presents new set of challenges relating to management of data. Data Management on Hadoop encompasses data motion (import/export), process orchestration (data pipelines, late/re-processing, scheduling), lifecycle management (retention, replication, DR, anonymization, archival), data discovery (data classification, Lineage), etc. among other concerns that are beyond ETL. The presentation focuses on a new data processing and management platform for Hadoop, Falcon that attempts to solve this problem by leveraging existing stacks in the Hadoop ecosystem. Falcon has been in production for nearly a year at InMobi and has been managing hundreds of feeds and processes.

published:11 Jul 2013

views:4035

Learn how to deal with the rapid growth of unstructured data. http://content.dell.com/us/en/enterprise/large-enterprise.aspx?~ck=mn&dgc=SM&cid=248506&lid=4318176

Data management

Data management comprises all the disciplines related to managing data as a valuable resource.

Overview

The official definition provided by DAMA International, the professional organization for those in the data management profession, is: "Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise." This definition is fairly broad and encompasses a number of professions which may not have direct technical contact with lower-level aspects of data management, such as relational database management.

Alternatively, the definition provided in the DAMA Data Management Body of Knowledge () is:
"Data management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets."

The concept of "Data Management" arose in the 1980s as technology moved from sequential processing (first cards, then tape) to random access processing. Since it was now technically possible to store a single fact in a single place and access that using random access disk, those suggesting that "Data Management" was more important than "Process Management" used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems." During this period, random access processing was not competitively fast, so those suggesting "Process Management" was more important than "Data Management" used batch processing time as their primary argument. As applications moved into real-time, interactive applications, it became obvious to most practitioners that both management processes were important. If the data was not well defined, the data would be mis-used in applications. If the process wasn't well defined, it was impossible to meet user needs.

Raw data, i.e. unprocessed data, is a collection of numbers, characters; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next. Field data is raw data that is collected in an uncontrolled in situ environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording.

The Latin word "data" is the plural of "datum", and still may be used as a plural noun in this sense. Nowadays, though, "data" is most commonly used in the singular, as a mass noun (like "information", "sand" or "rain").

Data Management Skills for Professionals, Julia Sutton - FIMA

We are DataManagement Professionals

We are so passionate about our field of work that we continually update our knowledge of products, hardware and other relevant IT developments. We realise that this can benefit you too, by sharing targeted information about value and necessity, pros and cons. Our professionals enjoy sharing their knowledge here on YouTube with you.
We also regularly organise knowledge sessions, events and trainings, on our own initiative or at your request. Visit our website: www.dm-p.com for more information.

3:29

Introduction to Data Management (Online Course - Preview)

Introduction to Data Management (Online Course - Preview)

Introduction to Data Management (Online Course - Preview)

“Introduction to Data Management” is designed for both, business and technology professionals, who are looking for understanding of data management fundamental aspects.

Journey to Modern Marketing – Episode 8: Data Management

ModernMark continues his Journey to Modern Marketing in Episode 8: Data Management. Watch him use data management and activation to create more meaningful customer interactions using Modern Marketing best practices and data-driven personalization.

The main challenge of Big Data is storing and processing the data at a specified time span. The traditional approach is not efficient in doing that. So Hadoop technologies and various Big Data tools have emerged to solve the challenges in Big Data environment. There are a lot of Big Data tools, all of them help in some or the other way in saving time, money and in covering business insights. This video will talk about such tools used in Big Data management.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Tools-Tutorial-Pyo4RWtxsQM&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

2:11

Mastering Big Data Management

Mastering Big Data Management

Mastering Big Data Management

Do you have the right data? Check. Do you have the skills onboard to effectively mine the data? Check. Then you might just be ready for predictive analytics.

42:10

Falcon - Data Management Platform on Hadoop (Beyond ETL)

Falcon - Data Management Platform on Hadoop (Beyond ETL)

Falcon - Data Management Platform on Hadoop (Beyond ETL)

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuable foresights that they never could have in the past. While it is easy to leverage Hadoop for crunching large volumes of data, organizing data, managing life cycle of data and processing data is fairly involved. This is solved adequately well in a traditional data platform involving data warehouses and standard ETL (extract-transform-load) tools, but remains largely unsolved today. Besides data processing complexities, Hadoop presents new set of challenges relating to management of data. Data Management on Hadoop encompasses data motion (import/export), process orchestration (data pipelines, late/re-processing, scheduling), lifecycle management (retention, replication, DR, anonymization, archival), data discovery (data classification, Lineage), etc. among other concerns that are beyond ETL. The presentation focuses on a new data processing and management platform for Hadoop, Falcon that attempts to solve this problem by leveraging existing stacks in the Hadoop ecosystem. Falcon has been in production for nearly a year at InMobi and has been managing hundreds of feeds and processes.

8:53

Data Management Strategy

Data Management Strategy

Data Management Strategy

Learn how to deal with the rapid growth of unstructured data. http://content.dell.com/us/en/enterprise/large-enterprise.aspx?~ck=mn&dgc=SM&cid=248506&lid=4318176

5:32

Agile for Data Professionals

Agile for Data Professionals

Agile for Data Professionals

This video clip was recorded live at Data ModelingZone (www.DataModelingZone.com). The full video is available on SafariBooks: http://bit.ly/2hXWSEf.
In recent years, there’s been intense debate about how (or whether) the principles of Agile development can/should be applied to data management work (including data modeling and database development). Now the Agile debate has shifted to BI development, raising questions of whether an incremental approach can be applied to enterprise-wide data work.
Larry Burns, author of Building the Agile Database, has been in the vanguard of Agile Data for over a decade. In his current role as Data and BI Architect for a global Fortune 500 company, he is also applying Agile principles to the development of his company’s BI architecture. In this workshop, Larry will be providing answers to the questions that all Data and BI professionals have about Agile.

Apache Spark is an open-source cluster-computing framework. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the ApacheSoftwareFoundation, which has maintained it since. Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance.
Big DataHadoop and Spark DeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Spark-QaoJNXW6SQo&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

3:00

Water Data Management – Faster Analysis. Better Decisions.

Water Data Management – Faster Analysis. Better Decisions.

Water Data Management – Faster Analysis. Better Decisions.

Today, water monitoring professionals are under more pressure than ever before. Stakeholders expect immediate online access to continuous water information that’s accurate, timely, & defensible. So what do you do? Learn how the dedicated team of hydrologists, scientists, and software engineers at Aquatic Informatics designed AQUARIUS: the world’s leading software suite for water data management.
Learn more about AQUARIUS here: aquaticinformatics.com

1:19

Environmental Data Management for Tribal Professionals

Environmental Data Management for Tribal Professionals

Environmental Data Management for Tribal Professionals

This is the first video in the online curriculum of environmental data management courses, from the Institute for Tribal Environmental Professionals' Tribal Air Monitoring SupportCenter.

16:31

Apache Kafka Tutorial | Big Data Tutorial For Beginners | Simplilearn

Apache Kafka Tutorial | Big Data Tutorial For Beginners | Simplilearn

Apache Kafka Tutorial | Big Data Tutorial For Beginners | Simplilearn

Apache Kafka is an open-source stream processing platform developed by the ApacheSoftwareFoundation written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. Its storage layer is essentially a "massively scalable pub/sub message queue architected as a distributed transaction log," making it highly valuable for enterprise infrastructures to process streaming data.
Big DataHadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Kafka-U4y2R3v9tlY&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

45:24

The Seven Deadly Sins of Data Management

The Seven Deadly Sins of Data Management

The Seven Deadly Sins of Data Management

Learn more: http://slrwnds.com/7Sins
All too often, data professionals are our nemesis when it comes to handling data and data management. Many data professionals and system administrators fail to recognize that the danger in our own habits increases the risk that the business will fall short of its goals. The danger may not be as destructive as an all-out data breach, but we are often to blame for enabling our business end-users to lust after BIGDATA, resulting in data hoarding that leads to ROT (redundant, outdated, trivial information).
So, while the world’s collective media shines a light on the never-ending list of security breaches, we suggest that there are common—and bigger—threats that data professionals need to guard against. Not all data professionals are guilty of every one of these sins. Rather, the collection of individuals who work in modern enterprise IT shops is culpable. HeadGeeks™ Thomas LaRock and Destiny Bertucci will share examples of data management, or rather, mismanagement.
Connect with SolarWinds:
THWACK IT Community: http://thwack.solarwinds.com/
Facebook: https://www.facebook.com/SolarWinds
Twitter: https://twitter.com/solarwinds
LinkedIn: http://www.linkedin.com/company/solarwinds
Instagram: http://instagram.com/solarwindsinc/
Flickr: http://www.flickr.com/photos/solarwinds_inc/

0:44

WGU Washington Launches Data Management and Analytics Degree Program

WGU Washington Launches Data Management and Analytics Degree Program

WGU Washington Launches Data Management and Analytics Degree Program

WGUWashington is now accepting applications for a new online bachelor’s degree program aimed at preparing IT professionals for roles in the growing field of data management and analytics. The Bachelor of Science in Data Management/Data Analytics is designed for experienced IT professionals seeking a bachelor’s degree and industry certifications to advance their careers.

Data Management Skills for Professionals, Julia Sutton - FIMA

We are DataManagement Professionals

We are so passionate about our field of work that we continually update our knowledge of products, hardware and other relevant IT developments. We realise that this can benefit you too, by sharing targeted information about value and necessity, pros and cons. Our professionals enjoy sharing their knowledge here on YouTube with you.
We also regularly organise knowledge sessions, events and trainings, on our own initiative or at your request. Visit our website: www.dm-p.com for more information.

published: 04 Jul 2014

Introduction to Data Management (Online Course - Preview)

“Introduction to Data Management” is designed for both, business and technology professionals, who are looking for understanding of data management fundamental aspects.

Journey to Modern Marketing – Episode 8: Data Management

ModernMark continues his Journey to Modern Marketing in Episode 8: Data Management. Watch him use data management and activation to create more meaningful customer interactions using Modern Marketing best practices and data-driven personalization.

The main challenge of Big Data is storing and processing the data at a specified time span. The traditional approach is not efficient in doing that. So Hadoop technologies and various Big Data tools have emerged to solve the challenges in Big Data environment. There are a lot of Big Data tools, all of them help in some or the other way in saving time, money and in covering business insights. This video will talk about such tools used in Big Data management.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Tools-Tutorial-Pyo4RWtxsQM&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigda...

published: 02 Nov 2017

Mastering Big Data Management

Do you have the right data? Check. Do you have the skills onboard to effectively mine the data? Check. Then you might just be ready for predictive analytics.

published: 12 May 2017

Falcon - Data Management Platform on Hadoop (Beyond ETL)

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuable foresights that they never could have in the past. While it is easy to leverage Hadoop for crunching large volumes of data, organizing data, managing life cycle of data and processing data is fairly involved. This is solved adequately well in a traditional data platform involving data warehouses and standard ETL (extract-transform-load) tools, but remains largely unsolved today. Besides data processing complexities, Hadoop presents new set of challenges relating to management of data. Data Management on Hadoop encompasses data motion (import/export), process orchestration (data pipelines, late/re-processing, scheduling), lifecycle...

published: 11 Jul 2013

Data Management Strategy

Learn how to deal with the rapid growth of unstructured data. http://content.dell.com/us/en/enterprise/large-enterprise.aspx?~ck=mn&dgc=SM&cid=248506&lid=4318176

published: 23 Aug 2011

Agile for Data Professionals

This video clip was recorded live at Data ModelingZone (www.DataModelingZone.com). The full video is available on SafariBooks: http://bit.ly/2hXWSEf.
In recent years, there’s been intense debate about how (or whether) the principles of Agile development can/should be applied to data management work (including data modeling and database development). Now the Agile debate has shifted to BI development, raising questions of whether an incremental approach can be applied to enterprise-wide data work.
Larry Burns, author of Building the Agile Database, has been in the vanguard of Agile Data for over a decade. In his current role as Data and BI Architect for a global Fortune 500 company, he is also applying Agile principles to the development of his company’s BI architecture. In this worksh...

Apache Spark is an open-source cluster-computing framework. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the ApacheSoftwareFoundation, which has maintained it since. Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance.
Big DataHadoop and Spark DeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Spark-QaoJNXW6SQo&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth ...

published: 13 Jul 2017

Water Data Management – Faster Analysis. Better Decisions.

Today, water monitoring professionals are under more pressure than ever before. Stakeholders expect immediate online access to continuous water information that’s accurate, timely, & defensible. So what do you do? Learn how the dedicated team of hydrologists, scientists, and software engineers at Aquatic Informatics designed AQUARIUS: the world’s leading software suite for water data management.
Learn more about AQUARIUS here: aquaticinformatics.com

published: 19 Apr 2016

Environmental Data Management for Tribal Professionals

This is the first video in the online curriculum of environmental data management courses, from the Institute for Tribal Environmental Professionals' Tribal Air Monitoring SupportCenter.

published: 06 Apr 2016

Apache Kafka Tutorial | Big Data Tutorial For Beginners | Simplilearn

Apache Kafka is an open-source stream processing platform developed by the ApacheSoftwareFoundation written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. Its storage layer is essentially a "massively scalable pub/sub message queue architected as a distributed transaction log," making it highly valuable for enterprise infrastructures to process streaming data.
Big DataHadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Kafka-U4y2R3v9tlY&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyt...

published: 25 Jul 2017

The Seven Deadly Sins of Data Management

Learn more: http://slrwnds.com/7Sins
All too often, data professionals are our nemesis when it comes to handling data and data management. Many data professionals and system administrators fail to recognize that the danger in our own habits increases the risk that the business will fall short of its goals. The danger may not be as destructive as an all-out data breach, but we are often to blame for enabling our business end-users to lust after BIGDATA, resulting in data hoarding that leads to ROT (redundant, outdated, trivial information).
So, while the world’s collective media shines a light on the never-ending list of security breaches, we suggest that there are common—and bigger—threats that data professionals need to guard against. Not all data professionals are guilty of every one ...

published: 06 Dec 2017

WGU Washington Launches Data Management and Analytics Degree Program

WGUWashington is now accepting applications for a new online bachelor’s degree program aimed at preparing IT professionals for roles in the growing field of data management and analytics. The Bachelor of Science in Data Management/Data Analytics is designed for experienced IT professionals seeking a bachelor’s degree and industry certifications to advance their careers.

We are DataManagement Professionals

We are so passionate about our field of work that we continually update our knowledge of products, hardware and other relevant IT developments. We realise that ...

We are so passionate about our field of work that we continually update our knowledge of products, hardware and other relevant IT developments. We realise that this can benefit you too, by sharing targeted information about value and necessity, pros and cons. Our professionals enjoy sharing their knowledge here on YouTube with you.
We also regularly organise knowledge sessions, events and trainings, on our own initiative or at your request. Visit our website: www.dm-p.com for more information.

We are so passionate about our field of work that we continually update our knowledge of products, hardware and other relevant IT developments. We realise that this can benefit you too, by sharing targeted information about value and necessity, pros and cons. Our professionals enjoy sharing their knowledge here on YouTube with you.
We also regularly organise knowledge sessions, events and trainings, on our own initiative or at your request. Visit our website: www.dm-p.com for more information.

ModernMark continues his Journey to Modern Marketing in Episode 8: Data Management. Watch him use data management and activation to create more meaningful customer interactions using Modern Marketing best practices and data-driven personalization.

ModernMark continues his Journey to Modern Marketing in Episode 8: Data Management. Watch him use data management and activation to create more meaningful customer interactions using Modern Marketing best practices and data-driven personalization.

The main challenge of Big Data is storing and processing the data at a specified time span. The traditional approach is not efficient in doing that. So Hadoop t...

The main challenge of Big Data is storing and processing the data at a specified time span. The traditional approach is not efficient in doing that. So Hadoop technologies and various Big Data tools have emerged to solve the challenges in Big Data environment. There are a lot of Big Data tools, all of them help in some or the other way in saving time, money and in covering business insights. This video will talk about such tools used in Big Data management.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Tools-Tutorial-Pyo4RWtxsQM&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

The main challenge of Big Data is storing and processing the data at a specified time span. The traditional approach is not efficient in doing that. So Hadoop technologies and various Big Data tools have emerged to solve the challenges in Big Data environment. There are a lot of Big Data tools, all of them help in some or the other way in saving time, money and in covering business insights. This video will talk about such tools used in Big Data management.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Tools-Tutorial-Pyo4RWtxsQM&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Falcon - Data Management Platform on Hadoop (Beyond ETL)

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuabl...

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuable foresights that they never could have in the past. While it is easy to leverage Hadoop for crunching large volumes of data, organizing data, managing life cycle of data and processing data is fairly involved. This is solved adequately well in a traditional data platform involving data warehouses and standard ETL (extract-transform-load) tools, but remains largely unsolved today. Besides data processing complexities, Hadoop presents new set of challenges relating to management of data. Data Management on Hadoop encompasses data motion (import/export), process orchestration (data pipelines, late/re-processing, scheduling), lifecycle management (retention, replication, DR, anonymization, archival), data discovery (data classification, Lineage), etc. among other concerns that are beyond ETL. The presentation focuses on a new data processing and management platform for Hadoop, Falcon that attempts to solve this problem by leveraging existing stacks in the Hadoop ecosystem. Falcon has been in production for nearly a year at InMobi and has been managing hundreds of feeds and processes.

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuable foresights that they never could have in the past. While it is easy to leverage Hadoop for crunching large volumes of data, organizing data, managing life cycle of data and processing data is fairly involved. This is solved adequately well in a traditional data platform involving data warehouses and standard ETL (extract-transform-load) tools, but remains largely unsolved today. Besides data processing complexities, Hadoop presents new set of challenges relating to management of data. Data Management on Hadoop encompasses data motion (import/export), process orchestration (data pipelines, late/re-processing, scheduling), lifecycle management (retention, replication, DR, anonymization, archival), data discovery (data classification, Lineage), etc. among other concerns that are beyond ETL. The presentation focuses on a new data processing and management platform for Hadoop, Falcon that attempts to solve this problem by leveraging existing stacks in the Hadoop ecosystem. Falcon has been in production for nearly a year at InMobi and has been managing hundreds of feeds and processes.

Agile for Data Professionals

This video clip was recorded live at Data ModelingZone (www.DataModelingZone.com). The full video is available on SafariBooks: http://bit.ly/2hXWSEf.
In rec...

This video clip was recorded live at Data ModelingZone (www.DataModelingZone.com). The full video is available on SafariBooks: http://bit.ly/2hXWSEf.
In recent years, there’s been intense debate about how (or whether) the principles of Agile development can/should be applied to data management work (including data modeling and database development). Now the Agile debate has shifted to BI development, raising questions of whether an incremental approach can be applied to enterprise-wide data work.
Larry Burns, author of Building the Agile Database, has been in the vanguard of Agile Data for over a decade. In his current role as Data and BI Architect for a global Fortune 500 company, he is also applying Agile principles to the development of his company’s BI architecture. In this workshop, Larry will be providing answers to the questions that all Data and BI professionals have about Agile.

This video clip was recorded live at Data ModelingZone (www.DataModelingZone.com). The full video is available on SafariBooks: http://bit.ly/2hXWSEf.
In recent years, there’s been intense debate about how (or whether) the principles of Agile development can/should be applied to data management work (including data modeling and database development). Now the Agile debate has shifted to BI development, raising questions of whether an incremental approach can be applied to enterprise-wide data work.
Larry Burns, author of Building the Agile Database, has been in the vanguard of Agile Data for over a decade. In his current role as Data and BI Architect for a global Fortune 500 company, he is also applying Agile principles to the development of his company’s BI architecture. In this workshop, Larry will be providing answers to the questions that all Data and BI professionals have about Agile.

Apache Spark is an open-source cluster-computing framework. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the ApacheSoftwareFoundation, which has maintained it since. Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance.
Big DataHadoop and Spark DeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Spark-QaoJNXW6SQo&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Apache Spark is an open-source cluster-computing framework. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the ApacheSoftwareFoundation, which has maintained it since. Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance.
Big DataHadoop and Spark DeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Spark-QaoJNXW6SQo&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Water Data Management – Faster Analysis. Better Decisions.

Today, water monitoring professionals are under more pressure than ever before. Stakeholders expect immediate online access to continuous water information that...

Today, water monitoring professionals are under more pressure than ever before. Stakeholders expect immediate online access to continuous water information that’s accurate, timely, & defensible. So what do you do? Learn how the dedicated team of hydrologists, scientists, and software engineers at Aquatic Informatics designed AQUARIUS: the world’s leading software suite for water data management.
Learn more about AQUARIUS here: aquaticinformatics.com

Today, water monitoring professionals are under more pressure than ever before. Stakeholders expect immediate online access to continuous water information that’s accurate, timely, & defensible. So what do you do? Learn how the dedicated team of hydrologists, scientists, and software engineers at Aquatic Informatics designed AQUARIUS: the world’s leading software suite for water data management.
Learn more about AQUARIUS here: aquaticinformatics.com

Apache Kafka is an open-source stream processing platform developed by the ApacheSoftwareFoundation written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. Its storage layer is essentially a "massively scalable pub/sub message queue architected as a distributed transaction log," making it highly valuable for enterprise infrastructures to process streaming data.
Big DataHadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Kafka-U4y2R3v9tlY&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Apache Kafka is an open-source stream processing platform developed by the ApacheSoftwareFoundation written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. Its storage layer is essentially a "massively scalable pub/sub message queue architected as a distributed transaction log," making it highly valuable for enterprise infrastructures to process streaming data.
Big DataHadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Kafka-U4y2R3v9tlY&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

The Seven Deadly Sins of Data Management

Learn more: http://slrwnds.com/7Sins
All too often, data professionals are our nemesis when it comes to handling data and data management. Many data profession...

Learn more: http://slrwnds.com/7Sins
All too often, data professionals are our nemesis when it comes to handling data and data management. Many data professionals and system administrators fail to recognize that the danger in our own habits increases the risk that the business will fall short of its goals. The danger may not be as destructive as an all-out data breach, but we are often to blame for enabling our business end-users to lust after BIGDATA, resulting in data hoarding that leads to ROT (redundant, outdated, trivial information).
So, while the world’s collective media shines a light on the never-ending list of security breaches, we suggest that there are common—and bigger—threats that data professionals need to guard against. Not all data professionals are guilty of every one of these sins. Rather, the collection of individuals who work in modern enterprise IT shops is culpable. HeadGeeks™ Thomas LaRock and Destiny Bertucci will share examples of data management, or rather, mismanagement.
Connect with SolarWinds:
THWACK IT Community: http://thwack.solarwinds.com/
Facebook: https://www.facebook.com/SolarWinds
Twitter: https://twitter.com/solarwinds
LinkedIn: http://www.linkedin.com/company/solarwinds
Instagram: http://instagram.com/solarwindsinc/
Flickr: http://www.flickr.com/photos/solarwinds_inc/

Learn more: http://slrwnds.com/7Sins
All too often, data professionals are our nemesis when it comes to handling data and data management. Many data professionals and system administrators fail to recognize that the danger in our own habits increases the risk that the business will fall short of its goals. The danger may not be as destructive as an all-out data breach, but we are often to blame for enabling our business end-users to lust after BIGDATA, resulting in data hoarding that leads to ROT (redundant, outdated, trivial information).
So, while the world’s collective media shines a light on the never-ending list of security breaches, we suggest that there are common—and bigger—threats that data professionals need to guard against. Not all data professionals are guilty of every one of these sins. Rather, the collection of individuals who work in modern enterprise IT shops is culpable. HeadGeeks™ Thomas LaRock and Destiny Bertucci will share examples of data management, or rather, mismanagement.
Connect with SolarWinds:
THWACK IT Community: http://thwack.solarwinds.com/
Facebook: https://www.facebook.com/SolarWinds
Twitter: https://twitter.com/solarwinds
LinkedIn: http://www.linkedin.com/company/solarwinds
Instagram: http://instagram.com/solarwindsinc/
Flickr: http://www.flickr.com/photos/solarwinds_inc/

WGU Washington Launches Data Management and Analytics Degree Program

WGUWashington is now accepting applications for a new online bachelor’s degree program aimed at preparing IT professionals for roles in the growing field of da...

WGUWashington is now accepting applications for a new online bachelor’s degree program aimed at preparing IT professionals for roles in the growing field of data management and analytics. The Bachelor of Science in Data Management/Data Analytics is designed for experienced IT professionals seeking a bachelor’s degree and industry certifications to advance their careers.

WGUWashington is now accepting applications for a new online bachelor’s degree program aimed at preparing IT professionals for roles in the growing field of data management and analytics. The Bachelor of Science in Data Management/Data Analytics is designed for experienced IT professionals seeking a bachelor’s degree and industry certifications to advance their careers.

The Seven Deadly Sins of Data Management

Learn more: http://slrwnds.com/7Sins
All too often, data professionals are our nemesis when it comes to handling data and data management. Many data professionals and system administrators fail to recognize that the danger in our own habits increases the risk that the business will fall short of its goals. The danger may not be as destructive as an all-out data breach, but we are often to blame for enabling our business end-users to lust after BIGDATA, resulting in data hoarding that leads to ROT (redundant, outdated, trivial information).
So, while the world’s collective media shines a light on the never-ending list of security breaches, we suggest that there are common—and bigger—threats that data professionals need to guard against. Not all data professionals are guilty of every one ...

published: 06 Dec 2017

File Station for MIS professionals - data & storage management on NAS

published: 30 Jan 2018

Hadoop for Data Warehousing professionals

Organizations across all industries are growing extremely fast, resulting in high volume of complex and unstructured data. The huge data generated is limiting the traditional Data Warehouse system, making it tougher for IT and data management professionals to handle the growing scale of data and analytical workload. The flow of data is so much more than what the existing Data Warehousing platforms can absorb and analyse. Looking at the expenses, the cost to scale traditional Data Warehousing technologies are high and insufficient to accommodate today's huge variety and volume of data. Therefore, the main reason behind organizations adopting Hadoop is that, it is a complete open-source data management system. Not only does it organize, store and process data (whether structured, semi-struct...

published: 31 May 2014

Big Data Specialist: Introduction to Big Data

JigsawAcademy (http://www.jigsawacademy.com and http://www.analyticstraining.com) presents a video on analytics.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

published: 28 Jul 2015

Falcon - Data Management Platform on Hadoop (Beyond ETL)

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuable foresights that they never could have in the past. While it is easy to leverage Hadoop for crunching large volumes of data, organizing data, managing life cycle of data and processing data is fairly involved. This is solved adequately well in a traditional data platform involving data warehouses and standard ETL (extract-transform-load) tools, but remains largely unsolved today. Besides data processing complexities, Hadoop presents new set of challenges relating to management of data. Data Management on Hadoop encompasses data motion (import/export), process orchestration (data pipelines, late/re-processing, scheduling), lifecycle...

published: 11 Jul 2013

Environmental Data Management

The fundamental structure for graph databases in big data is called “node-relationship.” This structure is most useful when you must deal with highly interconnected data. Nodes and relationships support properties, a key-value pair where the data is stored.
Big DataHadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Graph-aL9c_mZpqx8&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-li...

Data-Ed Online: Monetizing Data Management - Show Me The Money

Practicality and profitability may share a page in the dictionary, but incorporating both into a data management plan can prove challenging. Many data professionals struggle to demonstrate tangible returns on data management investments, especially in industries such as healthcare where financial results aren’t necessarily an organization’s primary concern. The key to “monetizing” data management, therefore, is thinking about data in a different way: as an information solution rather than simply an IT one, using data to drive decision-making towards increased profits and potentially alternative returns on investment or value outcomes as well. Taking a broader view of data assets facilitates easier sharing of information across organizational silos, and allows for a wider understanding of t...

This SAS Tutorial is specially designed for beginners, it starts with Why Data Analytics is needed, goes on to explain the various tools in Data Analytics, and why SAS is used among them, towards the end we will see how we can install SAS software and a short demo on the same!
In this SAS Tutorial video you will understand:
1) Why Data Analytics?
2) What is Data Analytics?
3) Data Science Analytics Tools
4) Why SAS?
5) What is SAS?
6) What SAS Solves?
7) Components of SAS
8) How can we practice Base SAS?
9) Demo
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete SAS Training playlist here: https://goo.gl/MMLyuN
#SASTraining #SASTutorial #SASCertification
How it Works?
1. There will be 30 hours of instructor-led interactive online clas...

Data-Ed Online Webinar: Monetizing Data Management

Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality init...

published: 13 Mar 2016

Introduction To Mapreduce | Hadoop Tutorial For Beginners

Big data and Hadoop developer course offered by Simplilearn, this lesson will introduce you to MapReduce and its operations. By the end of this lesson you will be able to
1. Explain the concepts of MapReduce
2. List the steps to install hadoop in Ubuntu machine
3. Explain the rules of user and system
Big-Data and Hadoop DeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Hadoop-mapreduce-fHWXRxB3UqU&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoo...

published: 10 Sep 2015

MapReduce In Hadoop | Hadoop Tutorial | Simplilearn

MapReduce is a core component of the ApacheHadoop software framework. Hadoop enables resilient, distributed processing of massive unstructured data sets across commodity computer clusters, in which each node of the cluster includes its own storage. MapReduce serves two essential functions: It parcels out work to various nodes within the cluster or map, and it organizes and reduces the results from each node into a cohesive answer to a query.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Mapreduce-rll6EnW95R8&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanal...

published: 20 Jul 2017

MongoDB For Big Data | Big Data Tutorial For Beginners | Simplilearn

MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemas. MongoDB is developed by MongoDB Inc., and is published under a combination of the GNU Affero General Public License and the ApacheLicense.
Big DataHadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-MongoDB-S3D5suhZ4bs&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing u...

Subscribing to this channel will help us promote more content.
Thank your for supporting our initiative.
https://goo.gl/s0cXtC
Videos from our Meetup about DataUnification In a CorporateEnvironment.
The European Data Innovation Hub is a contributing actor in the data innovation ecosystem and supports data professionals throughout Belgium with networking activities, events, training and meeting facilities, e-learning platform, co-working space and mentorship.
We foster grassroots community initiatives and take the burden out of organising them. As a facilitator and catalyst we support the plans and ambition of professionals, academics and government by helping them to connect, organise, share, learn and inspire.
http://datasciencebe.com/
https://twitter.com/Datasciencebe
http://ww...

published: 21 Oct 2015

Hadoop Yarn Tutorial | Hadoop Tutorial For Beginners | Simplilearn

Apache HadoopYARN (Yet AnotherResource Negotiator) is a cluster management technology. YARN is one of the key features in the second-generation Hadoop 2 version of the ApacheSoftwareFoundation's open source distributed processing framework.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Hadoop-Yarn-KqaPMCMHH4g&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial #HadoopTutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-l...

published: 25 Aug 2017

What Is Big Data | What Is Hadoop | Big Data Tutorial For Beginners

This video consists of four lessons of Big Data and Hadoop Tutorial. The lesson begins with the introduction of Big data and Hadoop developer and its objectives where you end up learning the fundamental concepts of hadoop, applying programming skills in MapReduce, Utilization of big data anlytic skills using pig and hive, Hbase data model and its components, and describes ZooKeeper and Sqoop.
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will lea...

The Seven Deadly Sins of Data Management

Learn more: http://slrwnds.com/7Sins
All too often, data professionals are our nemesis when it comes to handling data and data management. Many data profession...

Learn more: http://slrwnds.com/7Sins
All too often, data professionals are our nemesis when it comes to handling data and data management. Many data professionals and system administrators fail to recognize that the danger in our own habits increases the risk that the business will fall short of its goals. The danger may not be as destructive as an all-out data breach, but we are often to blame for enabling our business end-users to lust after BIGDATA, resulting in data hoarding that leads to ROT (redundant, outdated, trivial information).
So, while the world’s collective media shines a light on the never-ending list of security breaches, we suggest that there are common—and bigger—threats that data professionals need to guard against. Not all data professionals are guilty of every one of these sins. Rather, the collection of individuals who work in modern enterprise IT shops is culpable. HeadGeeks™ Thomas LaRock and Destiny Bertucci will share examples of data management, or rather, mismanagement.
Connect with SolarWinds:
THWACK IT Community: http://thwack.solarwinds.com/
Facebook: https://www.facebook.com/SolarWinds
Twitter: https://twitter.com/solarwinds
LinkedIn: http://www.linkedin.com/company/solarwinds
Instagram: http://instagram.com/solarwindsinc/
Flickr: http://www.flickr.com/photos/solarwinds_inc/

Learn more: http://slrwnds.com/7Sins
All too often, data professionals are our nemesis when it comes to handling data and data management. Many data professionals and system administrators fail to recognize that the danger in our own habits increases the risk that the business will fall short of its goals. The danger may not be as destructive as an all-out data breach, but we are often to blame for enabling our business end-users to lust after BIGDATA, resulting in data hoarding that leads to ROT (redundant, outdated, trivial information).
So, while the world’s collective media shines a light on the never-ending list of security breaches, we suggest that there are common—and bigger—threats that data professionals need to guard against. Not all data professionals are guilty of every one of these sins. Rather, the collection of individuals who work in modern enterprise IT shops is culpable. HeadGeeks™ Thomas LaRock and Destiny Bertucci will share examples of data management, or rather, mismanagement.
Connect with SolarWinds:
THWACK IT Community: http://thwack.solarwinds.com/
Facebook: https://www.facebook.com/SolarWinds
Twitter: https://twitter.com/solarwinds
LinkedIn: http://www.linkedin.com/company/solarwinds
Instagram: http://instagram.com/solarwindsinc/
Flickr: http://www.flickr.com/photos/solarwinds_inc/

Hadoop for Data Warehousing professionals

Organizations across all industries are growing extremely fast, resulting in high volume of complex and unstructured data. The huge data generated is limiting t...

Organizations across all industries are growing extremely fast, resulting in high volume of complex and unstructured data. The huge data generated is limiting the traditional Data Warehouse system, making it tougher for IT and data management professionals to handle the growing scale of data and analytical workload. The flow of data is so much more than what the existing Data Warehousing platforms can absorb and analyse. Looking at the expenses, the cost to scale traditional Data Warehousing technologies are high and insufficient to accommodate today's huge variety and volume of data. Therefore, the main reason behind organizations adopting Hadoop is that, it is a complete open-source data management system. Not only does it organize, store and process data (whether structured, semi-structured or unstructured), it is cost effective as well.
Hadoop's role in Data Warehousing is evolving rapidly. Initially, Hadoop was used as a transitory platform for extract, transform, and load (ETL) processing. In this role, Hadoop is used to offload processing and transformations performed in the data warehouse. You can visit the site edureka.in for more details on Big Data & Hadoop.
Hadoop simplifies your job as a Data Warehousing professional. With Hadoop, you can manage any volume, variety and velocity of data, flawlessly and comparably in less time. As a Data Warehousing professional, you will undoubtedly have troubleshooting and data processing skills. These skills are sufficient for you to be a proficient Hadoop-er.

Organizations across all industries are growing extremely fast, resulting in high volume of complex and unstructured data. The huge data generated is limiting the traditional Data Warehouse system, making it tougher for IT and data management professionals to handle the growing scale of data and analytical workload. The flow of data is so much more than what the existing Data Warehousing platforms can absorb and analyse. Looking at the expenses, the cost to scale traditional Data Warehousing technologies are high and insufficient to accommodate today's huge variety and volume of data. Therefore, the main reason behind organizations adopting Hadoop is that, it is a complete open-source data management system. Not only does it organize, store and process data (whether structured, semi-structured or unstructured), it is cost effective as well.
Hadoop's role in Data Warehousing is evolving rapidly. Initially, Hadoop was used as a transitory platform for extract, transform, and load (ETL) processing. In this role, Hadoop is used to offload processing and transformations performed in the data warehouse. You can visit the site edureka.in for more details on Big Data & Hadoop.
Hadoop simplifies your job as a Data Warehousing professional. With Hadoop, you can manage any volume, variety and velocity of data, flawlessly and comparably in less time. As a Data Warehousing professional, you will undoubtedly have troubleshooting and data processing skills. These skills are sufficient for you to be a proficient Hadoop-er.

JigsawAcademy (http://www.jigsawacademy.com and http://www.analyticstraining.com) presents a video on analytics.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

JigsawAcademy (http://www.jigsawacademy.com and http://www.analyticstraining.com) presents a video on analytics.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

Falcon - Data Management Platform on Hadoop (Beyond ETL)

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuabl...

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuable foresights that they never could have in the past. While it is easy to leverage Hadoop for crunching large volumes of data, organizing data, managing life cycle of data and processing data is fairly involved. This is solved adequately well in a traditional data platform involving data warehouses and standard ETL (extract-transform-load) tools, but remains largely unsolved today. Besides data processing complexities, Hadoop presents new set of challenges relating to management of data. Data Management on Hadoop encompasses data motion (import/export), process orchestration (data pipelines, late/re-processing, scheduling), lifecycle management (retention, replication, DR, anonymization, archival), data discovery (data classification, Lineage), etc. among other concerns that are beyond ETL. The presentation focuses on a new data processing and management platform for Hadoop, Falcon that attempts to solve this problem by leveraging existing stacks in the Hadoop ecosystem. Falcon has been in production for nearly a year at InMobi and has been managing hundreds of feeds and processes.

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuable foresights that they never could have in the past. While it is easy to leverage Hadoop for crunching large volumes of data, organizing data, managing life cycle of data and processing data is fairly involved. This is solved adequately well in a traditional data platform involving data warehouses and standard ETL (extract-transform-load) tools, but remains largely unsolved today. Besides data processing complexities, Hadoop presents new set of challenges relating to management of data. Data Management on Hadoop encompasses data motion (import/export), process orchestration (data pipelines, late/re-processing, scheduling), lifecycle management (retention, replication, DR, anonymization, archival), data discovery (data classification, Lineage), etc. among other concerns that are beyond ETL. The presentation focuses on a new data processing and management platform for Hadoop, Falcon that attempts to solve this problem by leveraging existing stacks in the Hadoop ecosystem. Falcon has been in production for nearly a year at InMobi and has been managing hundreds of feeds and processes.

The fundamental structure for graph databases in big data is called “node-relationship.” This structure is most useful when you must deal with highly interconne...

The fundamental structure for graph databases in big data is called “node-relationship.” This structure is most useful when you must deal with highly interconnected data. Nodes and relationships support properties, a key-value pair where the data is stored.
Big DataHadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Graph-aL9c_mZpqx8&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

The fundamental structure for graph databases in big data is called “node-relationship.” This structure is most useful when you must deal with highly interconnected data. Nodes and relationships support properties, a key-value pair where the data is stored.
Big DataHadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Graph-aL9c_mZpqx8&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

HadoopTraining - https://www.edureka.co/hadoop
This Edureka Big Data tutorial ( Big Data Hadoop Blog series: https://goo.gl/LFesy8 ) helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial:
1) Evolution of Data
2) What is Big Data?
3) Big Data as an Opportunity
4) Problems in Encasing Big Data Opportunity
5) Hadoop as a Solution
6) Hadoop Ecosystem
7) Edureka Big Data & Hadoop Training
Subscribe to our channel to get video updates. Hit the subscribe button above.
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
- - - - - - - - - - - - - -
How it Works?
1. This is a 5 WeekInstructor led OnlineCourse, 40 hours of assignment and 30 hours of project work
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka’s Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you:
1. Master the concepts of HDFS and MapReduce framework
2. Understand Hadoop 2.x Architecture
3. Setup Hadoop Cluster and write Complex MapReduce programs
4. Learn data loading techniques using Sqoop and Flume
5. Perform data analytics using Pig, Hive and YARN
6. Implement HBase and MapReduce integration
7. Implement Advanced Usage and Indexing
8. Schedule jobs using Oozie
9. Implement best practices for Hadoop development
10. Work on a real life Project on Big Data Analytics
11. Understand Spark and its Ecosystem
12. Learn how to work in RDD in Spark
- - - - - - - - - - - - - -
Who should go for this course?
If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career:
1. Analytics professionals
2. BI /ETL/DW professionals
3. Project managers
4. Testing professionals
5. Mainframe professionals
6. Software developers and architects
7. Recent graduates passionate about building successful career in Big Data
- - - - - - - - - - - - - -
Why Learn Hadoop?
Big Data! A WorldwideProblem?
According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success!
The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data.
- - - - - - - - - - - - - -
Opportunities for Hadoopers!
Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself!
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
CustomerReview:
Michael Harkins, System Architect, Hortonworks says: “The courses are top rate. The best part is live instruction, with playback. But my favourite feature is viewing a previous class. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! ~ This is the killer education app...I've take two courses, and I'm taking two more.”

HadoopTraining - https://www.edureka.co/hadoop
This Edureka Big Data tutorial ( Big Data Hadoop Blog series: https://goo.gl/LFesy8 ) helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial:
1) Evolution of Data
2) What is Big Data?
3) Big Data as an Opportunity
4) Problems in Encasing Big Data Opportunity
5) Hadoop as a Solution
6) Hadoop Ecosystem
7) Edureka Big Data & Hadoop Training
Subscribe to our channel to get video updates. Hit the subscribe button above.
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
- - - - - - - - - - - - - -
How it Works?
1. This is a 5 WeekInstructor led OnlineCourse, 40 hours of assignment and 30 hours of project work
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka’s Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you:
1. Master the concepts of HDFS and MapReduce framework
2. Understand Hadoop 2.x Architecture
3. Setup Hadoop Cluster and write Complex MapReduce programs
4. Learn data loading techniques using Sqoop and Flume
5. Perform data analytics using Pig, Hive and YARN
6. Implement HBase and MapReduce integration
7. Implement Advanced Usage and Indexing
8. Schedule jobs using Oozie
9. Implement best practices for Hadoop development
10. Work on a real life Project on Big Data Analytics
11. Understand Spark and its Ecosystem
12. Learn how to work in RDD in Spark
- - - - - - - - - - - - - -
Who should go for this course?
If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career:
1. Analytics professionals
2. BI /ETL/DW professionals
3. Project managers
4. Testing professionals
5. Mainframe professionals
6. Software developers and architects
7. Recent graduates passionate about building successful career in Big Data
- - - - - - - - - - - - - -
Why Learn Hadoop?
Big Data! A WorldwideProblem?
According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success!
The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data.
- - - - - - - - - - - - - -
Opportunities for Hadoopers!
Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself!
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
CustomerReview:
Michael Harkins, System Architect, Hortonworks says: “The courses are top rate. The best part is live instruction, with playback. But my favourite feature is viewing a previous class. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! ~ This is the killer education app...I've take two courses, and I'm taking two more.”

published:25 Apr 2017

views:322361

back

What is Big Data | What Is Hadoop and Big Data | Big Data Tutorial For Beginners | Simplilearn

This Big Data Tutorial will help you understand:
1. Big Data and HadoopDeveloperCourseIntroduction ( 0:07 )
2. Introduction to Big Data ( 3:31 )
3. Big Data Sources ( 7:48 )
4. Big Data Characteristics ( 8:30 )
5. Big Data Use Cases ( 13:00 )
6. Introduction to Hadoop ( 14:20 )
7. Hadoop History ( 15:04 )
8. Organizations using Hadoop ( 16:32 )
9. Hadoop Basics ( 17:50 )
10. VMPlayer Introduction ( 18:36 )
11. VMPlayer Installation ( 20:51 )
12. Hadoop Architecture ( 30:13 )
13. Hadoop Components ( 32:47 )
14. HDFS Characteristics ( 35:39 )
15. HDFS Features ( 37:20 )
16. HDFS Architecture ( 38:15 )
This Big Data Tutorial video consists of four lessons of Big Data and Hadoop Tutorial. The lesson begins with the introduction of Big data and Hadoop developer and its objectives where you end up learning the fundamental concepts of Hadoop, applying programming skills in MapReduce, Utilization of big data analytic skills using pig and hive, HBase data model and its components, and describes ZooKeeper and Sqoop.
Big-Data and Hadoop Developer CertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=What-is-Big-Data-What-is-Hadoop-CKLzDWMsQGM&utm_medium=Tutorials&utm_source=youtube
Watch the New Upgraded video: https://www.youtube.com/watch?v=zvKVfpIidG0
Big Data Tutorial Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
Prerequisite:
1. As the knowledge of Java is necessary for this course, we are providing a complimentary access to “Java Essentials for Hadoop” course
2. For Spark, we use Python and Scala and an Ebook has been provided to help you with the same
3. Knowledge of an operating system like Linux is useful for the course
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

This Big Data Tutorial will help you understand:
1. Big Data and HadoopDeveloperCourseIntroduction ( 0:07 )
2. Introduction to Big Data ( 3:31 )
3. Big Data Sources ( 7:48 )
4. Big Data Characteristics ( 8:30 )
5. Big Data Use Cases ( 13:00 )
6. Introduction to Hadoop ( 14:20 )
7. Hadoop History ( 15:04 )
8. Organizations using Hadoop ( 16:32 )
9. Hadoop Basics ( 17:50 )
10. VMPlayer Introduction ( 18:36 )
11. VMPlayer Installation ( 20:51 )
12. Hadoop Architecture ( 30:13 )
13. Hadoop Components ( 32:47 )
14. HDFS Characteristics ( 35:39 )
15. HDFS Features ( 37:20 )
16. HDFS Architecture ( 38:15 )
This Big Data Tutorial video consists of four lessons of Big Data and Hadoop Tutorial. The lesson begins with the introduction of Big data and Hadoop developer and its objectives where you end up learning the fundamental concepts of Hadoop, applying programming skills in MapReduce, Utilization of big data analytic skills using pig and hive, HBase data model and its components, and describes ZooKeeper and Sqoop.
Big-Data and Hadoop Developer CertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=What-is-Big-Data-What-is-Hadoop-CKLzDWMsQGM&utm_medium=Tutorials&utm_source=youtube
Watch the New Upgraded video: https://www.youtube.com/watch?v=zvKVfpIidG0
Big Data Tutorial Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
Prerequisite:
1. As the knowledge of Java is necessary for this course, we are providing a complimentary access to “Java Essentials for Hadoop” course
2. For Spark, we use Python and Scala and an Ebook has been provided to help you with the same
3. Knowledge of an operating system like Linux is useful for the course
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Data-Ed Online: Monetizing Data Management - Show Me The Money

Practicality and profitability may share a page in the dictionary, but incorporating both into a data management plan can prove challenging. Many data professio...

Practicality and profitability may share a page in the dictionary, but incorporating both into a data management plan can prove challenging. Many data professionals struggle to demonstrate tangible returns on data management investments, especially in industries such as healthcare where financial results aren’t necessarily an organization’s primary concern. The key to “monetizing” data management, therefore, is thinking about data in a different way: as an information solution rather than simply an IT one, using data to drive decision-making towards increased profits and potentially alternative returns on investment or value outcomes as well. Taking a broader view of data assets facilitates easier sharing of information across organizational silos, and allows for a wider understanding of the investment’s requirements and benefits.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/

Practicality and profitability may share a page in the dictionary, but incorporating both into a data management plan can prove challenging. Many data professionals struggle to demonstrate tangible returns on data management investments, especially in industries such as healthcare where financial results aren’t necessarily an organization’s primary concern. The key to “monetizing” data management, therefore, is thinking about data in a different way: as an information solution rather than simply an IT one, using data to drive decision-making towards increased profits and potentially alternative returns on investment or value outcomes as well. Taking a broader view of data assets facilitates easier sharing of information across organizational silos, and allows for a wider understanding of the investment’s requirements and benefits.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/

This SAS Tutorial is specially designed for beginners, it starts with Why Data Analytics is needed, goes on to explain the various tools in Data Analytics, and ...

This SAS Tutorial is specially designed for beginners, it starts with Why Data Analytics is needed, goes on to explain the various tools in Data Analytics, and why SAS is used among them, towards the end we will see how we can install SAS software and a short demo on the same!
In this SAS Tutorial video you will understand:
1) Why Data Analytics?
2) What is Data Analytics?
3) Data Science Analytics Tools
4) Why SAS?
5) What is SAS?
6) What SAS Solves?
7) Components of SAS
8) How can we practice Base SAS?
9) Demo
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete SAS Training playlist here: https://goo.gl/MMLyuN
#SASTraining #SASTutorial #SASCertification
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get LifetimeAccess to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a VerifiableCertificate!
- - - - - - - - - - - - - -
About the CourseThe SAS training course is designed to provide knowledge and skills to become a successful Analytics professional. It starts with the fundamental concepts of rules of SAS as a Language to an introduction to advanced SAS topics like SAS Macros.
- - - - - - - - - - - - - -
Why Learn SAS?
The Edureka SAS training certifies you as an ‘in demand’ SAS professional, to help you grab top paying analytics job titles with hands-on skills and expertise around data mining and management concepts.
SAS is the primary analytics tool used by some of the largest KPOs, Banks like American Express, Barclays etc., financial services irms like GE Money, KPOs like Genpact, TCS etc., telecom companies like Verizon (USA), consulting companies like Accenture, KPMG etc use the tool effectively.
- - - - - - - - - - - - - -
Who should go for this course?
This course is designed for professionals who want to learn widely acceptable data mining and exploration tools and techniques, and wish to build a booming career around analytics. The course is ideal for:
1. Analytics professionals who are keen to migrate to advanced analytics
2. BI /ETL/DW professionals who want to start exploring data to eventually become data scientist
3. Project Managers to help build hands-on SAS knowledge, and to become a SME via analytics
4. Testing professionals to move towards creative aspects of data analytics
5. Mainframe professionals
6. Software developers and architects
7. Graduates aiming to build a career in Big Data as a foundational step
Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.
Website: https://www.edureka.co/sas-training
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Sidharta Mitra, IBMMDMCOEHead @ CTS , says, "Edureka has been an unique and fulfilling experience. The course contents are up-to-date and the instructors are industry trained and extremely hard working. The support is always willing to help you out in various ways as promptly as possible. Edureka redefines the way online training is conducted by making it as futuristic as possible, with utmost care and minute detailing, packaged into the a unique virtual classrooms. Thank you Edureka!"

This SAS Tutorial is specially designed for beginners, it starts with Why Data Analytics is needed, goes on to explain the various tools in Data Analytics, and why SAS is used among them, towards the end we will see how we can install SAS software and a short demo on the same!
In this SAS Tutorial video you will understand:
1) Why Data Analytics?
2) What is Data Analytics?
3) Data Science Analytics Tools
4) Why SAS?
5) What is SAS?
6) What SAS Solves?
7) Components of SAS
8) How can we practice Base SAS?
9) Demo
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete SAS Training playlist here: https://goo.gl/MMLyuN
#SASTraining #SASTutorial #SASCertification
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get LifetimeAccess to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a VerifiableCertificate!
- - - - - - - - - - - - - -
About the CourseThe SAS training course is designed to provide knowledge and skills to become a successful Analytics professional. It starts with the fundamental concepts of rules of SAS as a Language to an introduction to advanced SAS topics like SAS Macros.
- - - - - - - - - - - - - -
Why Learn SAS?
The Edureka SAS training certifies you as an ‘in demand’ SAS professional, to help you grab top paying analytics job titles with hands-on skills and expertise around data mining and management concepts.
SAS is the primary analytics tool used by some of the largest KPOs, Banks like American Express, Barclays etc., financial services irms like GE Money, KPOs like Genpact, TCS etc., telecom companies like Verizon (USA), consulting companies like Accenture, KPMG etc use the tool effectively.
- - - - - - - - - - - - - -
Who should go for this course?
This course is designed for professionals who want to learn widely acceptable data mining and exploration tools and techniques, and wish to build a booming career around analytics. The course is ideal for:
1. Analytics professionals who are keen to migrate to advanced analytics
2. BI /ETL/DW professionals who want to start exploring data to eventually become data scientist
3. Project Managers to help build hands-on SAS knowledge, and to become a SME via analytics
4. Testing professionals to move towards creative aspects of data analytics
5. Mainframe professionals
6. Software developers and architects
7. Graduates aiming to build a career in Big Data as a foundational step
Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.
Website: https://www.edureka.co/sas-training
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Sidharta Mitra, IBMMDMCOEHead @ CTS , says, "Edureka has been an unique and fulfilling experience. The course contents are up-to-date and the instructors are industry trained and extremely hard working. The support is always willing to help you out in various ways as promptly as possible. Edureka redefines the way online training is conducted by making it as futuristic as possible, with utmost care and minute detailing, packaged into the a unique virtual classrooms. Thank you Edureka!"

Data-Ed Online Webinar: Monetizing Data Management

Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to bo...

Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
- Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution
- Take a broad view to ensure data sharing across organizational silos
- Start small and go for quick wins: Build momentum and support

Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
- Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution
- Take a broad view to ensure data sharing across organizational silos
- Start small and go for quick wins: Build momentum and support

Introduction To Mapreduce | Hadoop Tutorial For Beginners

Big data and Hadoop developer course offered by Simplilearn, this lesson will introduce you to MapReduce and its operations. By the end of this lesson you will ...

Big data and Hadoop developer course offered by Simplilearn, this lesson will introduce you to MapReduce and its operations. By the end of this lesson you will be able to
1. Explain the concepts of MapReduce
2. List the steps to install hadoop in Ubuntu machine
3. Explain the rules of user and system
Big-Data and Hadoop DeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Hadoop-mapreduce-fHWXRxB3UqU&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
Prerequisite:
1. As the knowledge of Java is necessary for this course, we are providing a complimentary access to “Java Essentials for Hadoop” course
2. For Spark we use Python and Scala and an Ebook has been provided to help you with the same
3. Knowledge of an operating system like Linux is useful for the course
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Big data and Hadoop developer course offered by Simplilearn, this lesson will introduce you to MapReduce and its operations. By the end of this lesson you will be able to
1. Explain the concepts of MapReduce
2. List the steps to install hadoop in Ubuntu machine
3. Explain the rules of user and system
Big-Data and Hadoop DeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Hadoop-mapreduce-fHWXRxB3UqU&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
Prerequisite:
1. As the knowledge of Java is necessary for this course, we are providing a complimentary access to “Java Essentials for Hadoop” course
2. For Spark we use Python and Scala and an Ebook has been provided to help you with the same
3. Knowledge of an operating system like Linux is useful for the course
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

MapReduce is a core component of the ApacheHadoop software framework. Hadoop enables resilient, distributed processing of massive unstructured data sets across commodity computer clusters, in which each node of the cluster includes its own storage. MapReduce serves two essential functions: It parcels out work to various nodes within the cluster or map, and it organizes and reduces the results from each node into a cohesive answer to a query.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Mapreduce-rll6EnW95R8&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

MapReduce is a core component of the ApacheHadoop software framework. Hadoop enables resilient, distributed processing of massive unstructured data sets across commodity computer clusters, in which each node of the cluster includes its own storage. MapReduce serves two essential functions: It parcels out work to various nodes within the cluster or map, and it organizes and reduces the results from each node into a cohesive answer to a query.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Mapreduce-rll6EnW95R8&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemas. MongoDB is developed by MongoDB Inc., and is published under a combination of the GNU Affero General Public License and the ApacheLicense.
Big DataHadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-MongoDB-S3D5suhZ4bs&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemas. MongoDB is developed by MongoDB Inc., and is published under a combination of the GNU Affero General Public License and the ApacheLicense.
Big DataHadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-MongoDB-S3D5suhZ4bs&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Subscribing to this channel will help us promote more content.
Thank your for supporting our initiative.
https://goo.gl/s0cXtC
Videos from our Meetup about Da...

Subscribing to this channel will help us promote more content.
Thank your for supporting our initiative.
https://goo.gl/s0cXtC
Videos from our Meetup about DataUnification In a CorporateEnvironment.
The European Data Innovation Hub is a contributing actor in the data innovation ecosystem and supports data professionals throughout Belgium with networking activities, events, training and meeting facilities, e-learning platform, co-working space and mentorship.
We foster grassroots community initiatives and take the burden out of organising them. As a facilitator and catalyst we support the plans and ambition of professionals, academics and government by helping them to connect, organise, share, learn and inspire.
http://datasciencebe.com/
https://twitter.com/Datasciencebe
http://www.datainnovationhub.eu/
https://twitter.com/Dataeu
https://twitter.com/Dataeu

Subscribing to this channel will help us promote more content.
Thank your for supporting our initiative.
https://goo.gl/s0cXtC
Videos from our Meetup about DataUnification In a CorporateEnvironment.
The European Data Innovation Hub is a contributing actor in the data innovation ecosystem and supports data professionals throughout Belgium with networking activities, events, training and meeting facilities, e-learning platform, co-working space and mentorship.
We foster grassroots community initiatives and take the burden out of organising them. As a facilitator and catalyst we support the plans and ambition of professionals, academics and government by helping them to connect, organise, share, learn and inspire.
http://datasciencebe.com/
https://twitter.com/Datasciencebe
http://www.datainnovationhub.eu/
https://twitter.com/Dataeu
https://twitter.com/Dataeu

Apache HadoopYARN (Yet AnotherResource Negotiator) is a cluster management technology. YARN is one of the key features in the second-generation Hadoop 2 version of the ApacheSoftwareFoundation's open source distributed processing framework.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Hadoop-Yarn-KqaPMCMHH4g&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial #HadoopTutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Apache HadoopYARN (Yet AnotherResource Negotiator) is a cluster management technology. YARN is one of the key features in the second-generation Hadoop 2 version of the ApacheSoftwareFoundation's open source distributed processing framework.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Hadoop-Yarn-KqaPMCMHH4g&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial #HadoopTutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

What Is Big Data | What Is Hadoop | Big Data Tutorial For Beginners

This video consists of four lessons of Big Data and Hadoop Tutorial. The lesson begins with the introduction of Big data and Hadoop developer and its objectives...

This video consists of four lessons of Big Data and Hadoop Tutorial. The lesson begins with the introduction of Big data and Hadoop developer and its objectives where you end up learning the fundamental concepts of hadoop, applying programming skills in MapReduce, Utilization of big data anlytic skills using pig and hive, Hbase data model and its components, and describes ZooKeeper and Sqoop.
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
Prerequisite:
1. As the knowledge of Java is necessary for this course, we are providing a complimentary access to “Java Essentials for Hadoop” course
2. For Spark we use Python and Scala and an Ebook has been provided to help you with the same
3. Knowledge of an operating system like Linux is useful for the course

This video consists of four lessons of Big Data and Hadoop Tutorial. The lesson begins with the introduction of Big data and Hadoop developer and its objectives where you end up learning the fundamental concepts of hadoop, applying programming skills in MapReduce, Utilization of big data anlytic skills using pig and hive, Hbase data model and its components, and describes ZooKeeper and Sqoop.
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
Prerequisite:
1. As the knowledge of Java is necessary for this course, we are providing a complimentary access to “Java Essentials for Hadoop” course
2. For Spark we use Python and Scala and an Ebook has been provided to help you with the same
3. Knowledge of an operating system like Linux is useful for the course

We are DataManagement Professionals

We are so passionate about our field of work that we continually update our knowledge of products, hardware and other relevant IT developments. We realise that this can benefit you too, by sharing targeted information about value and necessity, pros and cons. Our professionals enjoy sharing their knowledge here on YouTube with you.
We also regularly organise knowledge sessions, events and trainings, on our own initiative or at your request. Visit our website: www.dm-p.com for more information.

3:29

Introduction to Data Management (Online Course - Preview)

“Introduction to Data Management” is designed for both, business and technology profession...

Journey to Modern Marketing – Episode 8: Data Management

ModernMark continues his Journey to Modern Marketing in Episode 8: Data Management. Watch him use data management and activation to create more meaningful customer interactions using Modern Marketing best practices and data-driven personalization.

The main challenge of Big Data is storing and processing the data at a specified time span. The traditional approach is not efficient in doing that. So Hadoop technologies and various Big Data tools have emerged to solve the challenges in Big Data environment. There are a lot of Big Data tools, all of them help in some or the other way in saving time, money and in covering business insights. This video will talk about such tools used in Big Data management.
Big Data Hadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Tools-Tutorial-Pyo4RWtxsQM&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

2:11

Mastering Big Data Management

Do you have the right data? Check. Do you have the skills onboard to effectively mine the ...

Falcon - Data Management Platform on Hadoop (Beyond ETL)

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuable foresights that they never could have in the past. While it is easy to leverage Hadoop for crunching large volumes of data, organizing data, managing life cycle of data and processing data is fairly involved. This is solved adequately well in a traditional data platform involving data warehouses and standard ETL (extract-transform-load) tools, but remains largely unsolved today. Besides data processing complexities, Hadoop presents new set of challenges relating to management of data. Data Management on Hadoop encompasses data motion (import/export), process orchestration (data pipelines, late/re-processing, scheduling), lifecycle management (retention, replication, DR, anonymization, archival), data discovery (data classification, Lineage), etc. among other concerns that are beyond ETL. The presentation focuses on a new data processing and management platform for Hadoop, Falcon that attempts to solve this problem by leveraging existing stacks in the Hadoop ecosystem. Falcon has been in production for nearly a year at InMobi and has been managing hundreds of feeds and processes.

8:53

Data Management Strategy

Learn how to deal with the rapid growth of unstructured data. http://content.dell.com/us/e...

Agile for Data Professionals

This video clip was recorded live at Data ModelingZone (www.DataModelingZone.com). The full video is available on SafariBooks: http://bit.ly/2hXWSEf.
In recent years, there’s been intense debate about how (or whether) the principles of Agile development can/should be applied to data management work (including data modeling and database development). Now the Agile debate has shifted to BI development, raising questions of whether an incremental approach can be applied to enterprise-wide data work.
Larry Burns, author of Building the Agile Database, has been in the vanguard of Agile Data for over a decade. In his current role as Data and BI Architect for a global Fortune 500 company, he is also applying Agile principles to the development of his company’s BI architecture. In this workshop, Larry will be providing answers to the questions that all Data and BI professionals have about Agile.

Apache Spark is an open-source cluster-computing framework. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the ApacheSoftwareFoundation, which has maintained it since. Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance.
Big DataHadoop and Spark DeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Spark-QaoJNXW6SQo&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

The Seven Deadly Sins of Data Management

Learn more: http://slrwnds.com/7Sins
All too often, data professionals are our nemesis when it comes to handling data and data management. Many data professionals and system administrators fail to recognize that the danger in our own habits increases the risk that the business will fall short of its goals. The danger may not be as destructive as an all-out data breach, but we are often to blame for enabling our business end-users to lust after BIGDATA, resulting in data hoarding that leads to ROT (redundant, outdated, trivial information).
So, while the world’s collective media shines a light on the never-ending list of security breaches, we suggest that there are common—and bigger—threats that data professionals need to guard against. Not all data professionals are guilty of every one of these sins. Rather, the collection of individuals who work in modern enterprise IT shops is culpable. HeadGeeks™ Thomas LaRock and Destiny Bertucci will share examples of data management, or rather, mismanagement.
Connect with SolarWinds:
THWACK IT Community: http://thwack.solarwinds.com/
Facebook: https://www.facebook.com/SolarWinds
Twitter: https://twitter.com/solarwinds
LinkedIn: http://www.linkedin.com/company/solarwinds
Instagram: http://instagram.com/solarwindsinc/
Flickr: http://www.flickr.com/photos/solarwinds_inc/

Hadoop for Data Warehousing professionals

Organizations across all industries are growing extremely fast, resulting in high volume of complex and unstructured data. The huge data generated is limiting the traditional Data Warehouse system, making it tougher for IT and data management professionals to handle the growing scale of data and analytical workload. The flow of data is so much more than what the existing Data Warehousing platforms can absorb and analyse. Looking at the expenses, the cost to scale traditional Data Warehousing technologies are high and insufficient to accommodate today's huge variety and volume of data. Therefore, the main reason behind organizations adopting Hadoop is that, it is a complete open-source data management system. Not only does it organize, store and process data (whether structured, semi-structured or unstructured), it is cost effective as well.
Hadoop's role in Data Warehousing is evolving rapidly. Initially, Hadoop was used as a transitory platform for extract, transform, and load (ETL) processing. In this role, Hadoop is used to offload processing and transformations performed in the data warehouse. You can visit the site edureka.in for more details on Big Data & Hadoop.
Hadoop simplifies your job as a Data Warehousing professional. With Hadoop, you can manage any volume, variety and velocity of data, flawlessly and comparably in less time. As a Data Warehousing professional, you will undoubtedly have troubleshooting and data processing skills. These skills are sufficient for you to be a proficient Hadoop-er.

Big Data Specialist: Introduction to Big Data

JigsawAcademy (http://www.jigsawacademy.com and http://www.analyticstraining.com) presents a video on analytics.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

42:10

Falcon - Data Management Platform on Hadoop (Beyond ETL)

Hadoop and its ecosystem of products have made storing and processing massive amounts of d...

Falcon - Data Management Platform on Hadoop (Beyond ETL)

Hadoop and its ecosystem of products have made storing and processing massive amounts of data common place. This has enabled numerous businesses to gain valuable foresights that they never could have in the past. While it is easy to leverage Hadoop for crunching large volumes of data, organizing data, managing life cycle of data and processing data is fairly involved. This is solved adequately well in a traditional data platform involving data warehouses and standard ETL (extract-transform-load) tools, but remains largely unsolved today. Besides data processing complexities, Hadoop presents new set of challenges relating to management of data. Data Management on Hadoop encompasses data motion (import/export), process orchestration (data pipelines, late/re-processing, scheduling), lifecycle management (retention, replication, DR, anonymization, archival), data discovery (data classification, Lineage), etc. among other concerns that are beyond ETL. The presentation focuses on a new data processing and management platform for Hadoop, Falcon that attempts to solve this problem by leveraging existing stacks in the Hadoop ecosystem. Falcon has been in production for nearly a year at InMobi and has been managing hundreds of feeds and processes.

The fundamental structure for graph databases in big data is called “node-relationship.” This structure is most useful when you must deal with highly interconnected data. Nodes and relationships support properties, a key-value pair where the data is stored.
Big DataHadoop and SparkDeveloperCertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Graph-aL9c_mZpqx8&utm_medium=SC&utm_source=youtube
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

HadoopTraining - https://www.edureka.co/hadoop
This Edureka Big Data tutorial ( Big Data Hadoop Blog series: https://goo.gl/LFesy8 ) helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial:
1) Evolution of Data
2) What is Big Data?
3) Big Data as an Opportunity
4) Problems in Encasing Big Data Opportunity
5) Hadoop as a Solution
6) Hadoop Ecosystem
7) Edureka Big Data & Hadoop Training
Subscribe to our channel to get video updates. Hit the subscribe button above.
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
- - - - - - - - - - - - - -
How it Works?
1. This is a 5 WeekInstructor led OnlineCourse, 40 hours of assignment and 30 hours of project work
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
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About the Course
Edureka’s Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you:
1. Master the concepts of HDFS and MapReduce framework
2. Understand Hadoop 2.x Architecture
3. Setup Hadoop Cluster and write Complex MapReduce programs
4. Learn data loading techniques using Sqoop and Flume
5. Perform data analytics using Pig, Hive and YARN
6. Implement HBase and MapReduce integration
7. Implement Advanced Usage and Indexing
8. Schedule jobs using Oozie
9. Implement best practices for Hadoop development
10. Work on a real life Project on Big Data Analytics
11. Understand Spark and its Ecosystem
12. Learn how to work in RDD in Spark
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Who should go for this course?
If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career:
1. Analytics professionals
2. BI /ETL/DW professionals
3. Project managers
4. Testing professionals
5. Mainframe professionals
6. Software developers and architects
7. Recent graduates passionate about building successful career in Big Data
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Why Learn Hadoop?
Big Data! A WorldwideProblem?
According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success!
The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data.
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Opportunities for Hadoopers!
Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself!
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
CustomerReview:
Michael Harkins, System Architect, Hortonworks says: “The courses are top rate. The best part is live instruction, with playback. But my favourite feature is viewing a previous class. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! ~ This is the killer education app...I've take two courses, and I'm taking two more.”

52:20

What is Big Data | What Is Hadoop and Big Data | Big Data Tutorial For Beginners | Simplilearn

This Big Data Tutorial will help you understand:
1. Big Data and Hadoop Developer Course I...

What is Big Data | What Is Hadoop and Big Data | Big Data Tutorial For Beginners | Simplilearn

This Big Data Tutorial will help you understand:
1. Big Data and HadoopDeveloperCourseIntroduction ( 0:07 )
2. Introduction to Big Data ( 3:31 )
3. Big Data Sources ( 7:48 )
4. Big Data Characteristics ( 8:30 )
5. Big Data Use Cases ( 13:00 )
6. Introduction to Hadoop ( 14:20 )
7. Hadoop History ( 15:04 )
8. Organizations using Hadoop ( 16:32 )
9. Hadoop Basics ( 17:50 )
10. VMPlayer Introduction ( 18:36 )
11. VMPlayer Installation ( 20:51 )
12. Hadoop Architecture ( 30:13 )
13. Hadoop Components ( 32:47 )
14. HDFS Characteristics ( 35:39 )
15. HDFS Features ( 37:20 )
16. HDFS Architecture ( 38:15 )
This Big Data Tutorial video consists of four lessons of Big Data and Hadoop Tutorial. The lesson begins with the introduction of Big data and Hadoop developer and its objectives where you end up learning the fundamental concepts of Hadoop, applying programming skills in MapReduce, Utilization of big data analytic skills using pig and hive, HBase data model and its components, and describes ZooKeeper and Sqoop.
Big-Data and Hadoop Developer CertificationTraining: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=What-is-Big-Data-What-is-Hadoop-CKLzDWMsQGM&utm_medium=Tutorials&utm_source=youtube
Watch the New Upgraded video: https://www.youtube.com/watch?v=zvKVfpIidG0
Big Data Tutorial Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ
#bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatatutorial #bigdatahadoop #bigdataanalyticstutorial
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.
As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, AvroSchema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
16. Prepare for Cloudera Big Data CCA175 certification
Who should take this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. SeniorIT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
9. Graduates looking to build a career in Big Data Analytics
Prerequisite:
1. As the knowledge of Java is necessary for this course, we are providing a complimentary access to “Java Essentials for Hadoop” course
2. For Spark, we use Python and Scala and an Ebook has been provided to help you with the same
3. Knowledge of an operating system like Linux is useful for the course
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

1:07:57

Data-Ed Online: Monetizing Data Management - Show Me The Money

Practicality and profitability may share a page in the dictionary, but incorporating both ...

Data-Ed Online: Monetizing Data Management - Show Me The Money

Practicality and profitability may share a page in the dictionary, but incorporating both into a data management plan can prove challenging. Many data professionals struggle to demonstrate tangible returns on data management investments, especially in industries such as healthcare where financial results aren’t necessarily an organization’s primary concern. The key to “monetizing” data management, therefore, is thinking about data in a different way: as an information solution rather than simply an IT one, using data to drive decision-making towards increased profits and potentially alternative returns on investment or value outcomes as well. Taking a broader view of data assets facilitates easier sharing of information across organizational silos, and allows for a wider understanding of the investment’s requirements and benefits.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/

This SAS Tutorial is specially designed for beginners, it starts with Why Data Analytics is needed, goes on to explain the various tools in Data Analytics, and why SAS is used among them, towards the end we will see how we can install SAS software and a short demo on the same!
In this SAS Tutorial video you will understand:
1) Why Data Analytics?
2) What is Data Analytics?
3) Data Science Analytics Tools
4) Why SAS?
5) What is SAS?
6) What SAS Solves?
7) Components of SAS
8) How can we practice Base SAS?
9) Demo
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete SAS Training playlist here: https://goo.gl/MMLyuN
#SASTraining #SASTutorial #SASCertification
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get LifetimeAccess to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a VerifiableCertificate!
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About the CourseThe SAS training course is designed to provide knowledge and skills to become a successful Analytics professional. It starts with the fundamental concepts of rules of SAS as a Language to an introduction to advanced SAS topics like SAS Macros.
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Why Learn SAS?
The Edureka SAS training certifies you as an ‘in demand’ SAS professional, to help you grab top paying analytics job titles with hands-on skills and expertise around data mining and management concepts.
SAS is the primary analytics tool used by some of the largest KPOs, Banks like American Express, Barclays etc., financial services irms like GE Money, KPOs like Genpact, TCS etc., telecom companies like Verizon (USA), consulting companies like Accenture, KPMG etc use the tool effectively.
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Who should go for this course?
This course is designed for professionals who want to learn widely acceptable data mining and exploration tools and techniques, and wish to build a booming career around analytics. The course is ideal for:
1. Analytics professionals who are keen to migrate to advanced analytics
2. BI /ETL/DW professionals who want to start exploring data to eventually become data scientist
3. Project Managers to help build hands-on SAS knowledge, and to become a SME via analytics
4. Testing professionals to move towards creative aspects of data analytics
5. Mainframe professionals
6. Software developers and architects
7. Graduates aiming to build a career in Big Data as a foundational step
Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.
Website: https://www.edureka.co/sas-training
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Sidharta Mitra, IBMMDMCOEHead @ CTS , says, "Edureka has been an unique and fulfilling experience. The course contents are up-to-date and the instructors are industry trained and extremely hard working. The support is always willing to help you out in various ways as promptly as possible. Edureka redefines the way online training is conducted by making it as futuristic as possible, with utmost care and minute detailing, packaged into the a unique virtual classrooms. Thank you Edureka!"

1:21:32

Health Information Management: Microsoft Access For HIM Professionals

This is a recording of our Health Information Management webinar, Microsoft Office for HIM...

Data-Ed Online Webinar: Monetizing Data Management

Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
- Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution
- Take a broad view to ensure data sharing across organizational silos
- Start small and go for quick wins: Build momentum and support

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In August 2016, a research plane was able to observe something strange in the atmosphere above Alaska's Aleutian Islands, lingering aerosol particle that was enriched with the same kind of uranium used in nuclear fuel and bombs, according to Gizmodo. The observation was the first time that scientists detected a particle free-floating in the atmosphere in over 20 years of plane-based observations ... ... -WN.com, Maureen Foody....

ADDIS ABABA, Ethiopia (AP) -- Ethiopia's defense minister on Saturday ruled out a military takeover a day after the East African nation declared a new state of emergency amid the worst anti-government protests in a quarter-century ... Riots and protests have picked up again in Ethiopia since the end of the previous 10-month state of emergency, according to data compiled by the Armed Conflict Location & Event DataProject ... ....

One day in August 1995 a man called Foutanga Babani Sissoko walked into the head office of the Dubai Islamic Bank and asked for a loan to buy a car. The manager agreed, and Sissoko invited him home for dinner. It was the prelude, writes the BBC's Brigitte Scheffer, to one of the most audacious confidence tricks of all time. Over dinner, Sissoko made a startling claim ... With these powers, he could take a sum of money and double it ... ....

MEXICOCITY. A strong earthquake shook southern and central Mexico Friday, causing panic less than six months after two devastating quakes that killed hundreds of people. No buildings collapsed, according to early reports. But two towns near the epicenter, in the southern state of Oaxaca, reported damage and state authorities said they had opened emergency shelters ... It was also felt in the states of Guerrero, Puebla and Michoacan ... AFP ... ....

Mexico City – A military helicopter carrying officials assessing damage from a powerful earthquake crashed Friday in southern Mexico, killing 13 people and injuring 15, all of them on the ground. The Oaxaca state prosecutor’s office said in a statement that five women, four men and three children were killed at the crash site and another person died later at the hospital ...Alejandro Murat, neither of whom had serious injuries ... The U.S ... ....

Announcing a seemingly minor tweak through a Tweet, Google said it will remove the ViewImage button, reports Binjal Shah...Oracle is making heavy investments in its cloud infrastructure with the addition of 12 new data centre locations around the world, finds Vishal Krishna. Analysts say Oracle, which is entrenched in the enterprise space, will use this data centre expansion to help enterprises leverage the power of the cloud ... (L-R) ... ....

SACRAMENTO >> No one has the exact same definition of the California dream. Ask the 39 million Californians about the dream, and aside from most agreeing that the daily temperature should not dip lower than the mid-50 degrees, you likely will get 39 million different answers ... ....

Evincing excitement about the ‘convergence of data science and software’ in India, Bob Lord, ChiefDigitalOfficer of IBM, says the IT major is focusing on making India’s developer/startup community stronger ... His premise is that organisations would work with startups only if the data is within the firewall and if IBM can help them do that ... Finally, you need to know what you are going to do with this data....

As a two-day consultation over National HealthProtection Scheme (NHPS) drew to an end, several concerns emerged over the use of Socio-Economic Caste Census (SECC) data to identify and enrol beneficiaries.&nbsp; ... “The exercise will involve matching SECC data to other databases to cull out addresses,” an official at the consultation told DNA ... It is old data ... We cannot rely on such data.” ....

DES MOINES — Immigration arrests and deportations increased significantly during the past year in Iowa and its neighboring states, federal data shows ... Immigration arrests are up 67 percent and deportations 55 percent in the region that includes Iowa, according to the data ... That focus has produced significant increases in immigration arrests and deportations, according to federal data....

OLCC has developed initial processes to use this data to identify potential instances of noncompliance in the marijuana industry. However, auditors determined that immature regulatory processes and poor data quality increase the risk that compliance violations in the recreational marijuana program will go undetected ... • Reliance on self-reported data from marijuana businesses;....

I recently saw that the mobile provider Three offers an unlimited datasim card for €20 per month ...The Threedata offer isn't unlimited, it's 60GB per month (which the company describes as "all you can eat"). This is easily the best data capacity offer in the market (even if Three isn't the fastest 4G broadband service in the market) ... Netflix will use the lion's share of whatever data you consume....

The large-scale manufacturing (LSM) shrank for the second consecutive month posting a negative growth of 1.4 per cent year-on-year in December 2017, showed PakistanBureau of Statistics (PBS) data released on Saturday ... PBS data suggested ... Productiondata of 36 items received from the Ministry of Industries showed negative growth of 1.5pc in December....

NVIDIA has seen its data center business explode, producing triple-digit year-over-year growth for seven consecutive quarters, and its stock price has risen over 1,000% since early 2015... The company developed networking chips for data centers capable of transmitting greater quantities of data while consuming less power ... This vastly reduces the amount of user information that is transmitted to the cloud, which helps to secure the data....